We have been hearing this for quite a few years that Big Data and Analytics are the next big waves. While these waves are already sweeping us over, we are missing out on the small things going for the big. Big Data has emerged to be remarkably useful when it comes to finding answers to well-defined questions and addressing phenomena that are well understood. What it fails to recognize are the complicacy of peoples’ lives, human connections, underlying emotions, changing cultural ecosystems, interesting stories, and other social ingredients.
For instance, it made big news when Nokia was acquired by Microsoft in 2013. While there could be many reasons behind Nokia’s downfall, one of the prominent reasons that Tricia Wang, a Global Tech Ethnographer describes is the overdependence on numbers. Sharing her story on Ethnography Matters, she mentioned how her recommendations to Nokia to revise their product development strategy did not receive enough attention as the sample size used for her study was considered too small in comparison to millions of quantitative data that Nokia collected. Relying too much on quantitative data and failing to read the real sentiments of consumers essentially led to Nokia’s plummet. This is the void that can be filled up with Thick Data.
The Big Data Race
With consumers generating heaps of data every day, business organizations globally are clueless about how to deal with data, where to begin from, and how to generate actionable intelligence that could help them grow. They are increasingly turning towards Big Data Technologies in order to find meaning in the unstructured and raw consumer data. However, in this race to be data equipped, they fail to look beyond patterns, historical data, and standard frameworks. Steven Maxwell quotes that “People are getting caught up on the quantity side of the equation rather than the quality of the business insights that analytics can unearth.”
Having an abundance of quantitative data may not necessarily produce great insights. The recent US election is a classic example where traditional polls missed predicting a Trump government. The experts excessively relied on historical data and could not see important cultural shifts that took place over the years. They failed to see the changes in the emotions, the frustrations with the established institutions and other factors that led to the rise of Trump.
How Thick Data Can Fill the Void
Many business enterprises claim to be keeping customers at the focal point of their strategies but do they really look at customer data? If they do, how effective is their data in reflecting the true sentiments, social and emotional drivers, and aspirations of consumers?
For Big Data to be analyzable, it has to normalize, standardize, and define certain parameters and assumptions to sort, organize and disseminate information. The workability of its models depends on certain conjectures. If the underlying assumptions go wrong, the entire results may go wrong. This risk can be minimized using Thick Data. While Big Data relies on machine learning, Thick Data relies on the social context of connections between data points. It goes beyond big data to explaining everyday lives of consumers to understanding why they have a certain set of preferences. This way, it reduces the depreciation that the data goes through in order to become usable for analysis.
As organizations continue to invest in Big Data technologies, the budgets for human-centered research are taking a hit. It is highly imperative for them to pay due attention to ethnographic data to keep track of changing customer habits and mutual differences. The amalgamation of Big Data and Thick Data could create an impelling fuel for brands who keep customers at the center stage.
The Balancing Act
Robert Duke writes, “Big Data gives us an opportunity to derive useful insights and understand the correlations using several clearly defined questions. However, we must be able to establish the relevant questions. If we end up finding the right answers to the wrong questions, our big data investments are pointless.” This is where Thick Data can essentially bring a balance. Looking at things that are not measurable may help us frame the right questions and see beyond numbers. Trust, susceptibility, panic, greediness, desire, safety and other social, psychological and cultural factors can help us understand consumers from a larger perspective.
To summarize, business organizations should not get overwhelmed by Big Data and make systematic and coherent steps towards the same. They must remember that only numbers do not reflect the emotions and stories of consumers’ everyday lives and it’s vital to look at ethnographic data to understand the psychosocial factors influencing the numbers.
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